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Developing a Big Data Culture is Crucial for Banks to be Customer Centric

Developing a Big Data Culture is Crucial for Banks to be Customer Centric

It is important for banks to be customer centric because a customer who is better engaged is easier to retain and do business with. In order to provide services that are valued by customers, banks need to exploit big data. When big data is combined with analytics it can result in big opportunities for banks. The volume of banking customers is on the rise, and so is their data. It is time for the banking sector to look beyond traditional approaches and adopt new technologies powered by big data, like natural language processing and text mining, which help convert large amount of unstructured information into meaningful data that can lead to valuable insights.

Switching to big data enable banks to get a 360-degree view of their customers and keep providing excellent services. Many banks, like the Bank of America and U.S. Bank, have implemented big data analytics and are reaping its benefits. Rabobank, which has adopted big data analytics to detect criminal activity in ATMs, is ranked among the top 10 safest banks in the world.

Big Data’s Advantages for the Banking Industry:

  • Streamline Work Process and Service Delivery:

Banks need to filter through gazillions of data sets in order to provide relevant information to a customer, when he/she enters account details into the system. Big data can speed up this process. It enables financial institutes of spot and correct problems, before they affect clients. Big data also helps in cost-cuttings, which in turn lead to higher revenues for banks.

In case of erroneous clients, who tend to go back on their decisions, big data can help alter the process of  service delivery in such a manner that these clients are bound to stick to their commitments. It allows banks to track credit and loan limits, so that customers don’t exceed them.

Cloud based analytics packages sync in with big data systems to provide real-time evaluation. Banks can sift through tons of client information to track transactional behaviors in real time and provide relevant resources to clients. Real-time client contact is very useful in verifying suspicious transactions.

  • Customer Segmentation:

Big data help banks understand customer spending habits and determine their needs. For example, when we use our credit cards to purchase something, banks acquire information about what we purchase, how much we spend and use these information to provide relevant offers to us. Through big data, banks are able to trace all customer transactions and answer questions about a customer, like which services are commonly accessed, what are the preferred credit card expenditures and what his/her net worth is. The advantage of customer segmentation is that it enables banks to design marketing campaigns that cater to specific needs of a customer.  It can be used to deliver personalized schemes and plans. Analyzing the past and present expenses of a client helps bank create meaningful client relationships and improve response rates.

Let’s Take Your Data Dreams to the Next Level

  • Fraud detection:

According to Avivah Litan, a financial fraud expert at Gartner, big data supports behavioral authentication, which can help prevent fraud. Litan says, ‘’using big data to track such factors as how often a user typically accesses an account from a mobile device or PC, how quickly the user types in a username and password, and the geographic location from which the user most often accesses an account can substantially improve fraud detection.’’

Utah-based Zions Bank is largely dependent on big data to detect fraud. Big data can detect a complex problem like cross-channel fraud by aggregating fraud alerts from multiple disparate data sources and deriving meaningful insights from them. 

  • Risk Management:

Financial markets are becoming more and more interconnected, which increases their risk. Big data plays a pivotal role in risk management of financial sector as it provides more extensive risk coverage and faster responses. It helps create robust risk prediction models that evaluate credit repayment risks or determine the probability of default on loans for customers. It also aids in identifying risk associated with emergent financial technologies.

Hence, banks need to adopt a big data culture to improve customer satisfaction, keep up with global trends and generate higher revenues.

For credit risk management courses online, visit DexLab Analytics. It is a leading institute offering credit risk analytics training in Delhi.


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A Comprehensive Article on the Trends, Dynamics and Developments of Risk Analytics Market

Risk analytics makes organizations aware of the potential risks in their businesses. It helps companies make risk-aware decisions and improves their overall business performance. Risk analytics tools help investors get a better return on their capital and minimize the money required to be spent on regulatory compliances. Risk analytics tools aid in the central clearing of over-the-counter (OTC) derivatives.

Classification of Risk Analytics Market

Risk analytics market is divided based on:

  • Component type: Component segment of risk analytics market is further classified based on:
    • Type of solution: Risk analytics software group for regulatory compliance, market risk management, credit risk management, etc. are included in this group.
    • Services: Software services associated with risk analytics software like systems integration and risk evaluation are included in this group.
  • Size of enterprise: Risk analytics market based on size of enterprise is further categorized as:
    • Large organizations
    • Small and medium organizations
  • End-use verticals: Risk analytics market based on end-use vertical is further classified as:
    • BFSI- Banking, financial services and insurance
    • Manufacturing and retail
    • Telecom and IT
    • Government
    • Energy and utility, etc.

Risk analytics is expected to draw large revenues from BFSI sector. Recent times have seen developing countries perform better than the developed economies. This causes currency fluctuations and entails considerable risk. In the face of this current economic climate, BSFI sector is demanding improved risk analytics solution. State-of-art risk analytics tools are an absolute necessity for BSFI sector as they have to spot potential frauds using statistical models.

Main Drivers of Risk Analytics Market

  1. Market risk augmentation owing to:
  • Lack of economic stability
  • Market competitiveness
  1. Stringent regulations and policies are causing a surge in the demand of risk analytics software. Following are some policies responsible for the increased demand:
  • Basel I and II
  • Comprehensive Capital Analysis and Review
  • Dodd-Frank Wall Street Reform
  • Consumer Protection Act (CCAR/DFAST)

Small and medium sized enterprises lack cognizance of risk analytics tools. Moreover, a hefty amount of money is required for the installation of risk analytics tools. These issues are likely to hinder the growth of risk analytics market.

A developed IT sector and authoritative presence of blue chip companies are the key factors boosting the development of risk analytics market. North America is expected to hold majority of the market share in risk analytics market. Significant growth in risk analytics market is likely to occur in the Asia Pacific region. The growing competition in the market and fluctuations in currency will fuel the demand of risk analytic tools.

Major Vendors in Risk Analytics Market

  • IBM Corporation
  • SAP SE
  • Tata Consultancy Services Ltd.
  • SAS Institute
  • Oracle Corporation, etc.

With the rise in global risk, companies have to adopt new approaches to analyze risk. Big data and artificial intelligence are paving the way for the development of revolutionary strategies. CEOs are seeking the valuable input of insurers to curb the threat of cybercrime. Risk teams are turning into strategic advisors.

To know more about risk analytics follow Dexlab Analytics- a premium analytics training institute in Delhi. To gain proficiency in credit management tool, enroll for their credit risk modeling courses.

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How Credit Unions Can Capitalize on Data through Enterprise Integration of Data Analytics

credit risk analysis

To get valuable insights from the enormous quantity of data generated, credit unions need to move towards enterprise integration of data. This is a company-wide data democratization process that helps all departments within the credit union to manage and analyze their data. It allows each team member easy-access and proper utilization of relevant data.

However, awareness about the advantages of enterprise-wide data analytics isn’t sufficient for credit unions to deploy this system. Here is a three step guide to help credit unions get smarter in data handling.

Improve the quality of data

A robust and functional customer data set is of foremost importance. Unorganized data will hinder forming correct opinions about customer behavior. The following steps will ensure that relevant data enters the business analytics tools.

  • Integration of various analytics activity- Instead of operating separate analytics software for digital marketing, credit risk analytics, fraud detection and other financial activities, it is better to have a centralized system which integrates these activities. It is helpful for gathering cross-operational cognizance.
  • Experienced analytics vendors should be chosen- Vendors with experience can access a wide range of data. Hence, they can deliver information that is more valuable. They also provide pre-existing integrations.
  • Consider unconventional sources of data- Unstructured data from unconventional sources like social media and third-parties should be valued as it will prove useful in the future.
  • Continuous data cleansing that evolves with time- Clean data is essential for providing correct data. The data should be organized, error-free and formatted.

Data structure customized for credit unions

The business analytics tools for credit unions should perform the following analyses:

  • Analyzing the growth and fall in customers depending on their age, location, branch, products used, etc.
  • Measure the profit through the count of balances
  • Analyze the Performances of the staffs and members in a particular department or branch
  • Sales ratios reporting
  • Age distribution of account holders in a particular geographic location.
  • Perform trend analysis as and when required
  • Analyze satisfaction levels of members
  • Keep track of the transactions performed by members
  • Track the inquires made at call centers and online banking portals
  • Analyze the behavior of self-serve vs. non-self serve users based on different demographics
  • Determine the different types of accounts being opened and figure out the source responsible for the highest transactions.

User-friendly interfaces for manipulating data

Important decisions like growing revenue, mitigating risks and improving customer experience should be based on insights drawn using analytics tools. Hence, accessing the data should be a simple process. These following user-interface features will help make data user-friendly.

Dashboards- Dashboards makes data comprehensible even for non-techies as it makes data visually-pleasing. It provides at-a glance view of the key metrics, like lead generation rates and profitability sliced using demographics. Different datasets can be viewed in one place.

Scorecards- A scorecard is a type of report that compares a person’s performance against his goals. It measures success based on Key Performance Indicators (KPIs) and aids in keeping members accountable.

Automated reports- Primary stakeholders should be provided automated reports via mails on a daily basis so that they have access to all the relevant information.

Data analytics should encompass all departments of a credit union. This will help drawing better insights and improve KPI tracking. Thus, the overall performance of the credit union will become better and more efficient with time.

Technologies that help organizations draw valuable insights from their data are becoming very popular. To know more about these technologies follow Dexlab Analytics- a premier institute providing business analyst training courses in Gurgaon and do take a look at their credit risk modeling training course.

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How Credit Risk Modeling Is Used to Assess Credit Quality

Given the uproar on cyber crimes today, the issue of credit risk modeling is inevitable. Over the last few years, a wide number of globally recognized banks have initiated sophisticated systems to fabricate credit risk arising out of significant corporate details and disclosures. These adroit models are created with a sole intention to aid banks in determining, gauging, amassing and managing risk across encompassing business and product lines.


How Credit Risk Modeling Is Used to Assess Credit Quality


The more an institute’s portfolio expands better evaluation of individual credits is to be expected. Effective risk identification becomes the key factor to determine company growth. As a result, credit risk modeling backed by statistically-driven models and databases to support large volumes of data needs tends to be the need of the hour. It is defined as the analytical prudence that banks exhibit in order to assess the risk aspect of borrowers. The risk in question is dynamic, due to which the models need to assess the ability of a potential borrower if he can repay the loan along with taking a look at non-financial considerations, like environmental conditions, personality traits, management capabilities and more.

Continue reading “How Credit Risk Modeling Is Used to Assess Credit Quality”

Banks Merged With Fintech Startups to Perform Better Digitally

Axis Bank has acquired FreeCharge, a mobile wallet company opening doors to many such deals in the future. As a consequence, do you think banks and fintech startups have started working towards a common goal?

Banks Merged With Fintech Startups to Perform Better Digitally

On some day in the early 2016, Rajiv Anand, the Executive Director of Retail Banking at Axis Bank, asked his team who were hard at work, “Do present-day customers know how a bank would look in the future?”

Continue reading “Banks Merged With Fintech Startups to Perform Better Digitally”

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.

Help yourself with credit risk analysis course online at DexLab Analytics.

Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.

What are the challenges faced by CRM Managers?

  • Sloppy data management – Unable to access the data when it’s needed the most, due to inefficient data management issues.
  • No group-wide risk modeling framework – Banks need strong, meaningful risk measures to get a larger picture of the problem. Without these frameworks, it becomes really difficult to get to the tip of the problem.
  • Too much duplication of effort – As analysts cannot alter model parameters they face too much duplication of work, which results in constant rework. This may negatively affect a bank’s efficiency ratio.
  • Inefficient risk toolsBanks need to have a potent risk solution, otherwise how can they identify portfolio concentrations or re-grade portfolios to mitigate upcoming risks!
  • Long, unwieldy reporting processManual spreadsheet based reporting is simply horrible, overburdening the IT analysts and researchers.

What are the Best Practices to fight the Challenges Noted Above?

For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.

While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.

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Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:

  • Improved model management, stretching over the whole modeling life cycle
  • Real-time scoring and limits monitoring
  • Powerful stress-testing capabilities
  • Data visualization capabilities and robust BI tools that helps in transporting crucial information to anyone who needs them

In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.  

Get more insights on credit risk management including articles, research and other hot topics, follow us at DexLab Analytics. We offer excellent credit risk management courses in Delhi. For further queries, call us today!



Transformation of Smartphones with AI

Transformation of Smartphones with AI

Once a science fiction fantasy, Artificial Intelligence is today’s resonating reality. People are already relishing myriad advantages through advanced mobile apps and smart-forever smartphones.


Supposedly, smartphones made our lives easier. Not only does it allows us stay in touch with our beloved ones 24/7 but also allow us easy accessibility to a humungous amount of information over the internet, help us reach our designated destinations, play games, watch movies, check mails, and lot more.. And in the thick of all the telltales of new cameras and additional storage, AI is bringing in a poignant change in the realm of smartphone technology, which will impact our lives immensely.

Integrating AI with apps


How would you feel when your phone opens INSTAGRAM, before you tapped on it? You will be elated! Isn’t it? We are already witnessing some basic versions of above-mentioned technology on some phones where the most-used apps pop up on the top of the screen. It’s no more a thing from a science fiction novel; Google Now would know everything about you – the way you use your phone, when you call your home, when you need to tap open a map app, or even the exact moment you feel like taking a photo. No more you have to arrange your home screen icons or click on the apps you need, because whenever you will unlock your phone, the app you want to open will launch automatically.

Mark your steps with AI


 On the other hand, if we talk about mapping systems, you must have already come by Apple maps and Google maps that can predict your next whereabouts, based on past searches and destinations put into. In the near future, this technology will get cleverer intellectually. Making decisions based on your preferred routes, the type of public transport you board, how you react when you are stuck in traffic won’t be a tad difficult, provided Google has all the information it needs about you.

Say Hi to a digital assistant


Do you wonder at times, what if your phone becomes your best friend? Though it may sound creepy at first, but this is exactly the way towards which Artificial Intelligence is heading to. Digital assistants will be more like your best buddy who will be beside you on your happiest and worst days. If you feel stressed at work, your digital assistant will know how to uplift your mood or what kind of music to play to make you better..  


FELICITATIONS to the Personal assistant app


How about having an inbuilt personal assistant app to do the flight bookings or order some selected items from your shopping cart? Sounds cool! From restaurant bookings and comparing gas-energy prices to sending smart replies, this personal assistant using the bounties of AI excels on a bouquet of jobs.


Fulfilling software programs like Cortana, Siri and Google Now have already started bridging the gap between them and real-life personal assistants. In the future, this gap will further be lessened and these apps will finally be able to do many smart functions.

Get the best credit risk modeling training with our specialists at DexLab Analytics. Credit risk modelling certification course is in great demand now, so come to us and enrol.


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ANZ uses R programming for Credit Risk Analysis

ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

In the slides he made, one can see the following:

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

A comparison in between R and SAS for fitting such non-standard models

Mr. Ooi also notes that SAS does contain various options for modelling variance like for instance, SAS PROC MIXED, PRIC NLIN. However, none of these are as flexible or powerful as R. The main difference as per Ooi, is that R modelling functions return as object as opposed to returning with a mere textual output. This however, can be later modified and manipulated with to adapt to a new modelling situation and generate summaries, predictions and more. An R programmer can do this manipulation.


Read Also: From dreams to reality: a vision to train the youngsters about big data analytics by the young entrepreneurs:


We can use cohort models to aggregate the point estimates for default into an overall risk portfolio as follows:

A comparison in between R and SAS for fitting such non-standard models
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He revealed how ANZ implemented a stress-testing simulation, which made available to business users via an Excel interface:

The primary analysis is done in r programming within 2 minutes usually, in comparison to SAS versions that actually took 4 hours to run, and frequently kept crashing due to lack of disk space. As the data is stored within SAS; SAS code is often used to create the source data…

While an R script can be used to automate the process of writing, the SAS code can do so with much simplicity around the flexible limitations of SAS.


Read Also: Dexlab Analytics' Workshop on Sentiment Analysis of Twitter Data Using R Programming


Comparison between use of R and SAS’s IML language to implement algorithms:

Mr. Ooi’s R programming code has a neat trick of creating a matrix of R list objects, which is fairly difficult to do with IML’s matrix only data structures.

He also discussed some of the challenges one ma face when trying to deploy open-source R in the commercial organizations, like “who should I yell at if things do now work right”.

And lastly he also discussed a collection of typically useful R resources as well.

For people who work in a bank and need help adopting R in the workflow, may make use of this presentation to get some knowledge about the same. And also feel free to get in touch with our in-house experts in R programming at DexLab Analytics, the premiere R programming training institute in India.




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The Opportunities and Challenges in Credit Scoring with Big Data

The Opportunities and Challenges in Credit Scoring with Big Data

Within the past few decades, the banking institutions have collected plenty of data in order to describe the default behaviour of their clientele. Good examples of them are historical data about a person’s date of birth, their income, gender, status of employment etc. the whole of this data has all been nicely stored into several huge databases or data warehouses (for e.g. relational).

And on top of all this, the banks have accumulated several business experiences about their crediting products. For instance, a lot of credit experts have done a pretty swell job at discriminating between low risk and high risk mortgages with the use of their business mortgages, thereby making use of their business expertise only. It is now the goal of all credit scoring to conduct a detailed analysis of both the sources of data into a more detailed perspective with then come up with a statistically based decision model, which allows to score future credit applications and then ultimately make a decision about which ones to accept and which to reject.

With the surfacing of Big Data it has created both chances as well as challenges to conduct credit scoring. Big Data is often categorised in terms of its four Vs viz: Variety, Velocity, Volume, and Veracity. To further illustrate this, let us in short focus into some key sources or processes, which will generate Big Data.  

The traditional sources of Big Data are usually large scale transactional enterprise systems like OLTP (online Transactional Processing), ERP (Enterprise Resource Processing) and CRM (Customer Relationship Management) applications. The classical credit is generally constructed using the data extracted from these traditional transactional systems.

However, the online graphing is more recent example. Simply think about the all the major social media networks like, Weibo, Wechat, Facebook, Twitter etc. All of these networks together capture the information about close to two billion people relating to their friends preferences and their other behaviours, thereby leaving behind a huge trail of digital footprint in the form of data.

Also think about the IoT (the internet of things) or the emergence of the sensor enable ecosystems which is going to link the various objects (for e.g. cars, homes etc) with each other as well as with other humans. And finally, we get to see a more and more transparent or public data such as the data about weather, maps, traffic and the macro-economy. It is a clear indication that all of these new sources of generating data will offer a tremendous potential for building better credit scoring models.

The main challenges:

The above mentioned data generating processes can all be categorised in terms of their sheer volume of the data which is being created. Thus, it is evident that this poses to be a serious challenge in order to set up a scalable storage architecture which when combined with a distributed approach to manipulate data and query will be difficult.

Big Data also comes with a lot of variety or in several other formats. The traditional data or the structured data, such as customer name, their birth date etc are usually more and more complementary with unstructured data such as images, tweets, emails, sensor data, Facebook pages, GPS data etc. While the former may be easily stored in traditional databases, the latter needs to be accommodated with the use of appropriate database technology thus, facilitating the storage, querying and manipulation of each of these types of unstructured data. Also it requires a lot of effort since it is thought to be that at least 80 percent of all data in unstructured.

The speed at which data is generated is the velocity factor and it is at that perfect speed that it must be analysed and stored. You can imagine the streaming applications like on-line trading platforms, SMS messages, YouTube, about the credit card swipes and other phone calls, these are all examples of high velocity data and form an important concern.

Veracity which is the quality or trustworthiness of the data, is yet another factor that needs to be considered. However, sadly more data does not automatically indicate better data, so the quality of data being generated must be monitored closely and guaranteed.

So, in closing thoughts as the velocity, veracity, volume, and variety keeps growing, so will the new opportunities to build better credit scoring models.     

Looking for credit risk modelling courses? Take up our credit risk management course online or classroom-based from DexLab Analytics and get your career moving….



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